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  <section id="struct-input">
<span id="exhale-struct-structtorch-tensorrt-1-1input"></span><h1>Struct Input<a class="headerlink" href="#struct-input" title="Permalink to this headline">¶</a></h1>
<ul class="simple">
<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
</ul>
<section id="inheritance-relationships">
<h2>Inheritance Relationships<a class="headerlink" href="#inheritance-relationships" title="Permalink to this headline">¶</a></h2>
<section id="base-type">
<h3>Base Type<a class="headerlink" href="#base-type" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">public</span> <span class="pre">CustomClassHolder</span></code></p></li>
</ul>
</section>
</section>
<section id="struct-documentation">
<h2>Struct Documentation<a class="headerlink" href="#struct-documentation" title="Permalink to this headline">¶</a></h2>
<dl class="cpp struct">
<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5InputE">
<span class="target" id="structtorch__tensorrt_1_1Input"></span><span class="k"><span class="pre">struct</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><span class="n"><span class="pre">CustomClassHolder</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5InputE" title="Permalink to this definition">¶</a><br /></dt>
<dd><p>A struct to hold an input range (used by TensorRT Optimization profile) </p>
<p>This struct can either hold a single vector representing an input shape, signifying a static input shape or a set of three input shapes representing the min, optiminal and max input shapes allowed for the engine. </p>
<div class="breathe-sectiondef docutils container">
<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
<dl class="cpp function">
<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEv">
<span class="target" id="structtorch__tensorrt_1_1Input_1ae217f6c7c13be354e03d3f9b5b6b5a5e"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEv" title="Permalink to this definition">¶</a><br /></dt>
<dd></dd></dl>

<dl class="cpp function">
<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE12TensorFormat">
<span class="target" id="structtorch__tensorrt_1_1Input_1a0328d70f3daf18b425d459689be2591a"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from vector, optional arguments allow the user to configure expected input shape tensor format. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>shape</strong> – <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> tensor shape </p></li>
<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="cpp function">
<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE8DataType12TensorFormat">
<span class="target" id="structtorch__tensorrt_1_1Input_1af8b7efdf0e96b4221742a658b214738b"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from vector, optional arguments allow the user to configure expected input shape tensor format. </p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>shape</strong> – <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> tensor shape </p></li>
<li><p><strong>dtype</strong> – Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32) </p></li>
<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="cpp function">
<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE12TensorFormat">
<span class="target" id="structtorch__tensorrt_1_1Input_1a70d54a3caadadab68faad230ff302f23"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from c10::ArrayRef (the type produced by tensor.sizes()), vector, optional arguments allow the user to configure expected input shape tensor format dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>shape</strong> – <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> tensor shape </p></li>
<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="cpp function">
<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat">
<span class="target" id="structtorch__tensorrt_1_1Input_1a7069ee5a6db2f840423a923d08d08c6f"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from c10::ArrayRef (the type produced by tensor.sizes()), vector, optional arguments allow the user to configure expected input shape tensor format. </p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>shape</strong> – <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> tensor shape </p></li>
<li><p><strong>dtype</strong> – Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32) </p></li>
<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="cpp function">
<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE12TensorFormat">
<span class="target" id="structtorch__tensorrt_1_1Input_1a6f305b4f28ce0a7c76d39ed61dc9a964"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>min_shape</strong> – Minimum shape for input tensor </p></li>
<li><p><strong>opt_shape</strong> – Target optimization shape for input tensor </p></li>
<li><p><strong>max_shape</strong> – Maximum acceptible shape for input tensor </p></li>
<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="cpp function">
<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE8DataType12TensorFormat">
<span class="target" id="structtorch__tensorrt_1_1Input_1a93370fc5773212005972056184a527a5"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for a dynamic input size from vectors for minimum shape, optimal shape, and max shape supported sizes optional arguments allow the user to configure expected input shape tensor format. </p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>min_shape</strong> – Minimum shape for input tensor </p></li>
<li><p><strong>opt_shape</strong> – Target optimization shape for input tensor </p></li>
<li><p><strong>max_shape</strong> – Maximum acceptible shape for input tensor </p></li>
<li><p><strong>dtype</strong> – Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32) </p></li>
<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="cpp function">
<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE12TensorFormat">
<span class="target" id="structtorch__tensorrt_1_1Input_1abda696b9f2f689af6f3577e51647ad6f"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>min_shape</strong> – Minimum shape for input tensor </p></li>
<li><p><strong>opt_shape</strong> – Target optimization shape for input tensor </p></li>
<li><p><strong>max_shape</strong> – Maximum acceptible shape for input tensor </p></li>
<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="cpp function">
<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat">
<span class="target" id="structtorch__tensorrt_1_1Input_1a59eb3e2d3455017d19c9b498eefbb35e"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. </p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>min_shape</strong> – Minimum shape for input tensor </p></li>
<li><p><strong>opt_shape</strong> – Target optimization shape for input tensor </p></li>
<li><p><strong>max_shape</strong> – Maximum acceptible shape for input tensor </p></li>
<li><p><strong>dtype</strong> – Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32) </p></li>
<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="cpp function">
<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN2at6TensorE">
<span class="target" id="structtorch__tensorrt_1_1Input_1a3b472eb488183b47f470507860b49f1c"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">at</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">Tensor</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">tensor</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN2at6TensorE" title="Permalink to this definition">¶</a><br /></dt>
<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object using a torch tensor as an example The tensor’s shape, type and layout inform the spec’s values. </p>
<p>Note: You cannot set dynamic shape through this method, you must use an alternative constructor</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>tensor</strong> – Reference tensor to set shape, type and layout </p>
</dd>
</dl>
</dd></dl>

</div>
<div class="breathe-sectiondef docutils container">
<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-members">Public Members</p>
<dl class="cpp var">
<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input9min_shapeE">
<span class="target" id="structtorch__tensorrt_1_1Input_1af95ee167b1e25e1a599a8fc67ffea9ac"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">min_shape</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input9min_shapeE" title="Permalink to this definition">¶</a><br /></dt>
<dd><p>Minimum acceptable input size into the engine. </p>
</dd></dl>

<dl class="cpp var">
<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input9opt_shapeE">
<span class="target" id="structtorch__tensorrt_1_1Input_1a63666d30decf34115f642e75f0cf8256"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">opt_shape</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input9opt_shapeE" title="Permalink to this definition">¶</a><br /></dt>
<dd><p>Optimal input size into the engine (size optimized for given kernels accept any size in min max range) </p>
</dd></dl>

<dl class="cpp var">
<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input9max_shapeE">
<span class="target" id="structtorch__tensorrt_1_1Input_1ad3dfdcaaa9126dcb8dfa08dec085d589"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">max_shape</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input9max_shapeE" title="Permalink to this definition">¶</a><br /></dt>
<dd><p>Maximum acceptable input size into the engine. </p>
</dd></dl>

<dl class="cpp var">
<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5shapeE">
<span class="target" id="structtorch__tensorrt_1_1Input_1a26997c30b04c8013a5afadb181b70377"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">shape</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5shapeE" title="Permalink to this definition">¶</a><br /></dt>
<dd><p><a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> shape to be fed to TensorRT, in the event of a dynamic shape, -1’s will hold the place of variable dimensions </p>
</dd></dl>

<dl class="cpp var">
<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5dtypeE">
<span class="target" id="structtorch__tensorrt_1_1Input_1a05d520340d486851d9e8a4797611895e"></span><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dtype</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5dtypeE" title="Permalink to this definition">¶</a><br /></dt>
<dd><p>Expected data type for the input. </p>
</dd></dl>

<dl class="cpp var">
<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input6formatE">
<span class="target" id="structtorch__tensorrt_1_1Input_1ad544c1a1584d2f45bccf0e4963b8d292"></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">format</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input6formatE" title="Permalink to this definition">¶</a><br /></dt>
<dd><p>Expected tensor format for the input. </p>
</dd></dl>

</div>
</dd></dl>

</section>
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